Predictive Impact Model: Tool Overview

In today’s competitive business landscape, employee engagement is more crucial than ever. Yet, identifying and prioritizing the right areas to improve engagement can be challenging. 15Five’s Predictive Impact Model revolutionizes this process, giving HR leaders and managers precise insights into the factors that influence engagement. Leveraging advanced machine learning, this model allows organizations to make data-driven decisions that drive meaningful improvements in employee satisfaction, productivity, and retention. Here’s an in-depth look at how the Predictive Impact Model works, its benefits, and how to use it to build a thriving workplace.

In this article, you will learn...

• What is the Predictive Impact Model? 🤖
• How it works 🛠️
• Key components 🔑
• Benefits 🌟
• How to use it 🧑‍💻
• Frequently Asked Questions (FAQs)❓

What is the Predictive Impact Model? 🤖

15Five’s Predictive Impact Model is an AI-powered data analysis tool designed to pinpoint which engagement survey statements have the most significant influence on overall engagement scores. Instead of simply identifying low scores, the Predictive Impact Model assesses which driver statements, if improved, would most effectively boost engagement. It provides Predictive Impact Scores for each survey statement, showing the potential impact on engagement for specific employee groups.

The model is based on 15Five’s extensive dataset of over 600,000 completed engagement surveys, allowing it to detect patterns and correlations that reveal the factors most likely to increase employee engagement.

How it works 🛠️

The Predictive Impact Model works in two main phases: Prediction and Explanation.

1. Prediction: The model analyzes responses to all engagement survey statements, using a decision tree-based regression algorithm. It calculates how changes in individual statement responses are likely to influence overall engagement scores for each employee. This baseline engagement score represents a typical level of engagement within 15Five’s database, making it realistic and achievable.

2. Explanation: After identifying impactful statements, the model assigns a Predictive Impact Score to each, reflecting the potential point change in engagement if responses to that statement are improved. For instance, a statement with a -2.5 score indicates that low agreement with this statement is significantly detracting from engagement. By improving it, engagement can be expected to increase by roughly 2.5 points.

This combination of prediction and explanation allows leaders to see not just where engagement might be low, but specifically where improvements will create the biggest positive change.

Example

Consider a technology company where the Engineering Department has a lower-than-average engagement score. The Predictive Impact Model highlights two key statements with negative impact scores: “My job activities are personally meaningful to me.” and “There is a great support system at this organization that helps me achieve my work goals.” These driver statements are associated with the "Meaning" and "Goal Support" drivers, respectively.

Based on these insights, the company can launch strategic Action Plans to improve how the Engineering Department perceives these statements by sharing how engineering projects impact the company’s mission and addressing resource gaps for this team. Upon the conclusion of this Action Plan, HR can launch a new engagement campaign to see how it moved the needle on the organization's engagement score and these specific statement scores and use those insights to make further refinements.

Key components 🔑

• Predictive Impact Scores: The core output of the model, these scores quantify the effect each survey statement has on engagement, enabling leaders to prioritize actions that will have the most significant impact.
• Group-Specific Analysis: The model calculates impact scores at the individual level and aggregates them by teams, departments, or other custom segments, making it easy to identify areas of improvement across different parts of the organization.
• Quick, Scalable Insights: Unlike traditional engagement analyses that may take time and manual analysis, the Predictive Impact Model delivers insights quickly, allowing leaders to access real-time, actionable data.

Benefits of the Predictive Impact Model 🌟

1. Prioritizes Strategic Action: Not all low scores are equal. The Predictive Impact Model highlights the statements that are not just low-scoring, but also have the greatest influence on engagement. This helps HR leaders target their efforts for maximum impact.

2. Targets Specific Engagement Drivers: By analyzing individual statements within engagement drivers (such as autonomy, purpose, and manager support), the model helps organizations address precise engagement drivers that matter most to their employees.

3. Supports Informed Decision-Making: With clear, data-backed insights, the Predictive Impact Model minimizes guesswork, enabling HR teams to make well-informed, strategic decisions that align with organizational goals.

4. Enhances Continuous Improvement: The Predictive Impact Model is built to be used in each engagement survey cycle, providing a continuous feedback loop. As improvements are made, leaders can track the resulting changes in engagement scores, adjusting strategies as needed.

How to use it 🧑‍💻

1. Identify Impactful Statements: Begin by reviewing the Predictive Impact Scores in your Engagement Reports. These scores are available in the "Statements" tab of engagement survey results, where each statement is assigned a score based on its potential to improve engagement.

2. Analyze by Group or Team: Use the model’s group-specific insights to see which statements most impact engagement in specific departments, teams, or demographics. This enables a tailored approach where each group’s unique needs are prioritized.

3. Develop Targeted Action Plans: With the most impactful statements identified, HR leaders and managers can create Action Plans to address these specific areas. For example, if the statement “I get sufficient feedback about how well I am doing” has a high negative impact score, HR can focus on initiatives to improve feedback quality within that team.

4. Monitor and Adjust: Use the Predictive Impact Model over time to assess the effectiveness of your actions. As scores improve, you can adjust focus areas and continue building on successful initiatives, creating a continuous cycle of engagement improvement.

Frequently Asked Questions (FAQs)❓

What elements were used to measure the impact of drivers on engagement in the past?

In the past, we used influence bubbles to help you see how much impact specific drivers had on engagement. Influence bubbles were based on statistical analysis (Pearson bivariate correlation) between the individual driver and the organization's Engagement Score. 

A big, filled-in Influence bubble indicated that the Driver was highly correlated with engagement at your organization, and changes for this Driver were more likely to impact your Engagement Score. A small Influence bubble indicated that the Driver was not as well correlated with engagement at your organization, and changes for this driver would be less likely to influence your Engagement Score.